Media assets are often consumed by a group of people, but given the large amount of media available today, it can be difficult to choose a suitable media asset that appeals to everyone's preferences in the group and takes into account prior viewing histories. Traditional systems offer search features that may be used to browse for desirable media assets, but these traditional systems require the user to manually browse a list of assets and lack the ability to target the group with media assets that are relevant to each individual in the group. For example, one user may have watched the first five minutes of a television program, a second user may have watched the first ten minutes of the same television program, and a third user may have watched the last five minutes of the same television program. A recommendation to the first and the second users may be appropriate based on their common viewing of the first five minutes, but a recommendation for the media asset to all three users may not be ideal because the third user has not seen the first five minutes of the program.